Three subsets of sequence complexity and their relevance to biopolymeric information |
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Authors: | David?L?Abel Email author" target="_blank">Jack?T?TrevorsEmail author |
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Institution: | (1) Director, The Gene Emergence Project, The Origin-of-Life Foundation, Inc., 113 Hedgewood Dr., 20770-1610 Greenbelt, MD, USA;(2) Professor, Department of Environmental Biology, University of Guelph, Rm 3220 Bovey Building, N1G 2W1 Guelph, Ontario, Canada |
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Abstract: | Genetic algorithms instruct sophisticated biological organization. Three qualitative kinds of sequence complexity exist: random
(RSC), ordered (OSC), and functional (FSC). FSC alone provides algorithmic instruction. Random and Ordered Sequence Complexities
lie at opposite ends of the same bi-directional sequence complexity vector. Randomness in sequence space is defined by a lack
of Kolmogorov algorithmic compressibility. A sequence is compressible because it contains redundant order and patterns. Law-like
cause-and-effect determinism produces highly compressible order. Such forced ordering precludes both information retention
and freedom of selection so critical to algorithmic programming and control. Functional Sequence Complexity requires this
added programming dimension of uncoerced selection at successive decision nodes in the string. Shannon information theory
measures the relative degrees of RSC and OSC. Shannon information theory cannot measure FSC. FSC is invariably associated
with all forms of complex biofunction, including biochemical pathways, cycles, positive and negative feedback regulation,
and homeostatic metabolism. The algorithmic programming of FSC, not merely its aperiodicity, accounts for biological organization.
No empirical evidence exists of either RSC of OSC ever having produced a single instance of sophisticated biological organization.
Organization invariably manifests FSC rather than successive random events (RSC) or low-informational self-ordering phenomena
(OSC). |
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